Landslide hazard and risk assessment for road network using rs and gis

LANDSLIDE HAZARD AND RISK ASSESSMENT FOR ROAD NETWORK USING RS AND GIS: A CASE STUDY OF XIN MAN DISTRICT, VIET NAM by Lai Tuan Anh A thesis submitted in partial fulfillment of the requ

LANDSLIDE HAZARD AND RISK ASSESSMENT FOR ROAD

NETWORK USING RS AND GIS: A CASE STUDY OF XIN MAN

DISTRICT, VIET NAM

by

Lai Tuan Anh

A thesis submitted in partial fulfillment of the requirements for the

degree of Master of Engineering

Examinat ion Committee: Dr Kiyoshi Honda (Chairperson)

Dr Marc Souris

Dr Ulrich Glawe

Nationality:

Previous Degree:

Vietnamese Bachelor of Engineering in Geodesy Hanoi University of Mining and Geology,

Vietnam

Scholarship Donor: AIT Fellowship

Asian Institute of Technology School of Engineering Technology

Thailand May 2006

ACKNOWLEDGEMENT

It is with delight that the author first of all extends his hearties gratitude to the Thesis research committee Chairperson, DR Kiyoshi Honda for his professional guidance, advice, encouragement throughout the study

The technical and conceptual support of Dr Marc Souris, thesis committee member, helped me

to conduct the research for which I express my thanks to him

Valuable suggestions support of Dr Ulrich Glawe and thesis committee member help me to work enthusiastically so I am grateful to him

I would like to express my sincere thanks to DANIDA for the scholarship and Star program for the research grand, thereby making this study possible

Special thanks go to RSL staff, Mr Do Minh Phuong for providing all the necessary on time

I am grateful to the local in Xin Man province who provided and guide me go to all the landslide points to measurement GPS

My vote of thanks goes to all my friends, Mr Tran Trung Kien, Miss Dao Thi Chau Ha, for their helps, supports, sharing the difficulties to my life in AIT

Most of all, I want to express my deep appreciation to my family: Parents, my sister for their endless love, constant support and encouragement for the graduate study

ABSTRACT

Xin Man district in the South west Ha Giang has high landslide hazard However, the available information on landslide in Xin Man district is still limited We constructed the essential spatial database of landslides using GIS techniques The quantitative relationships between landslides and factor s affecting landslides are established by the Certainty Factor (CF) The affecting factors such as slope, elevation, landcover, geology, road distance, lineament distance, drainage density are recognized By applying CF value integration and landslide zonation, the most significant affecting factors are selected

By using RS&GIS technology landslide occurrences on all these factors have been analyzed The vector based GIS has been used for digitizing to produce thematic maps, as analysis for study was based on the pixel based information therefore Raster based GIS has been used for the analysis

Pixel based calculation was made by using the CF value Model By using the CF model we obtain the CF value for all classes al all factor maps On the basis of these CF value all factor maps are recoded and matrix analysis was perform to produce a Landslide Hazard Zonation map

The Landslide Hazard Zonation map has been applied to develop a methodology to produce hazard maps considering the behavior of landslide and to evaluate potential damage to infrastructure specific road system Different factors have been cons idered for this study

TABLE OF CONTENTS (CONT.)

5.1 Characterize several types of landslide in Xin Man district 44

5.4 Accuracy Check for Landslide Hazard Zonation Map 60 5.5 Develop a methodology to produce hazard maps considering the behavior of

5.6 Publish Landslide Hazard Zonation to Internet using Web Map Server 69

LIST OF TABLES

2.1 Landsat 7 ETM image characteristic 7 3.1 Geology, major litho-stratigraphic units with their corresponding classes 20

3.5 Area under distance to lineament 26

4.1 Analysis data from different sources 34

5.2 CF value of distance to lineament 48

5.8 The hazard value ranges used for road buffer 55 5.9 The hazard value ranges used for whole area 55 5.10 % area for landslide hazard zone for buffer area 56 5.11 % area for landslide hazard zone for whole area 56

5.14 Result of the risk class based on buffer analysis 69

LIST OF FIGURES

2.1 Criteria for risk assessment (Disaster Preparedness and Mitigation-2002) 4 2.2 Spectral reflectance of vegetation, soil and water 6 2.3 Spectral reflectance of a green left 7 2.4 The image interpretation processing 8

2.6 The flow of geometric correction 10

2.9 Map of large Landslide areas of Vietnam 17 3.1 The yearly rainfall from 1961 to 2003 18 3.2 Location of Study area Xin Man district, Viet Nam 19

3.5 Slope chart in Xin Man district 24 3.6 Distance to the lineament chart 26

4.1 Flo w Diagram For Landcover Map 35 4.2 Flow Diagram for Digitized Map 35 4.3 Flow Diagram for Landslide Map using GPS 36 4.4 Flow Diagram For TIN and maps extraction from TIN 37 4.5 Flow Diagram For Landcover Map extracted From Satellite Data 37 4.6 Flow Diagram for Buffered Road and lineament Maps 38 4.7 Methodology of thematic data layer preparation 39 5.1 Show the landslide attacked road 44 5.2 Wedge slip occur along the road 45

5.4 CF value of distance to lineament 49 5.5 Statistical map of slope angle distribution in Xin Man District 50

5.8 CF value of drainage density layer 52

5.11 Bar chart showing the distribution of various hazard zones 57

5.12 Bar chart showing the distribution of various hazard zones for whole area in Xin

5.13 Relative distributions of various hazard zones and landslide probability within

each zone in road buffer in Xin Man district 60

LIST OF FIGURES

5.14 Relative distributions of various hazard zones and landslide probability within

5.15 The description of the road buffer 62

5.17 Schematization the Landslide area 63 5.18 Flow chart fo r procedure risk map 64 5.19 Minnesota Mapserver Framework Using CGI 70

5.22 LHZ map in Xin Man on MapBrowser 74

LIST OF MAPS

3.2 Geological in Xin Man district 22 3.3 Elevation in Xin Man district 23

3.5 Distance to lineament in Xin Man 27 3.6 Buffer road system in Xin Man 28

5.1 Landslide distribution in Xin Man district 46 5.2 Landslide hazard zonation for buffer area in Xin Man 58 5.3 Landslide hazard zonation for buffer area in Xin Man 59 5.3 Risk of slope in Xin Man’s Road Network 65 5.4 Risk of distance to Xin Man’s road network 65

5.6 Risk assessment for road network in Xin Man 68

LIST OF ABBREVIATIONS

TIN Triangulated Irregular Network

DEM Digital Elevation Model

DTM Digital terrain Model

RS Remote Sensing

GIS Geographical Information System

GPS Global Position System

GML Geographical Mark Up Language

JPEG Join Photographic Experts Group

URL Uniform Resource Locator

WWW World Wide Web

WMS Web Map Service

WFS Web Feature Service

CHAPTER 1 INTRODUCTION 1.1 Background

Landslide has become one of the world’s major natural disasters for the few years in many countries Landslides are the most common natural hazard in mountainous terrain Landslide can be a major threat to population in the mountainous area Even when they occur away from the inhabited areas, landslide can be a significant hazard and have a serious economic impact

by blocking roads and river (Aniya, 1985; J Achache, B Fruneau and C Delacourt 1995) Landslides are widespread in many countries and cause great economic losses, especially when engineering constructions are designed and erected without heeding the stability conditions of the slopes (Q.Zaruba, V.Mencl 1967)

Landslides become a problem when they interfere with human activity The frequency and the magnitude of the slope failures can be increased due to human activities such as deforestation or urban expansion

Landslide hazard analysis is a difficult task It requires large number of parameters and techniques for analysis Remote sensing and GIS are the powerful analysis tools to handle this type of problems A in the analysis of landslide spatial information e.g topography, geology, landcover, etc are involved, therefore application of Remote sensing and GIS will be effective

1.2 Statement of problem

- Although landslide usually occur in Xin Man district, but people who live near or in the landslide’s local do not illustrate the different between them But actually, there are many types of landslide which can occur and each of them have separate characterize We need to give some information to describe characterize some types of landslide in Xin Man district

- Landslide is a serious disaster in Viet Nam In recent 10 years, there are more than 10 areas occurred violent landslide, causing above 300 human deaths and thousands of hectares

of solids was buried by stone, sand, pebble and hundreds of inhabitant settlements having to change their living places and locations.These are responsible for considerably greater socio-economic loss than is generally recognized There are some projects a nd research applying for landslide but only for mid_center of Viet nam Up to now, there is not hazard map, risk map about landslide in Xin Man district, the leader of province only have measure to prevent landslide every year and they have not had any project to study about landslide in the Xin Man district Hence, there is an urgent need to prepare landslide hazard zonation maps in the highly landslide susceptible mountainous terrain special is Xin Man district

- No other landslide investigation or risk assessment has been performed in Xin Man district to date

- Understanding and prevent landslide hazard is very important for every people What can people do when lack of information about natural hazard? Nowadays, internet is popular and useful for every people People can update, download all information and all thing which they need to know In this regard, we need to publish and share information about landslide on Internet by using Web Map Sever

1.3 Objectives

The general objective of this study is using Remote sensing and GIS technique to making landslide hazard zonation mapping in Xin Man district

The specific objectives of the study are:

1 Characterize several types of landslide in Xin Man district

2 Create zoning maps for landslide hazard that usually occurs in Xin Man district

3 Develop a methodology to produce hazard maps considering the behavior of landslides

4 Publish and share landslide hazard zonation map’s information on internet using

Web Map Server

1.4 Scope and limitation

- Landslide hazard map zonation will be focuses on critical physical factors by using GIS overlaying thematic maps

- To determine and localize area have high risk of landslide base on investigation, study topology, geology, hydrology, and geomorphology

- The limitation is associated with the availability of reliable and adequate data sets from secondary sources to support making landslide hazard zonation map

- Risk assessment only for road networks, not consider about the others as population, economics, socia l…

- Data collection is not enough to be analysis

- Landsat TM images will be used for analysis of landcover of the study area

- Apply existing program to publish landslide hazard zonation map on internet using Web Map Server

CHAPTER 2 LITERARUTE REVIEW

Natural Hazard is extreme events in the earth’s ecosystem The concepts of hazard, risk, and vulnerability are often confused with one another and with the extreme event itself Although the extreme event is inherent in hazard, risk and vulnerability terminology, it is not synonymous with the terminology Therefore it is necessary to distinguish between the terms

hazard, risk and vulnerability

Hazard assessment determines the type of hazardous phenomenon, frequency, magnitude and the extent of the area that may be affected Vulnerability indicates the degree of loss caused to people, infrastructure, buildings, economies etc… distinguishing physical (buildings, infrastructure), functional (lifelines, communication) and social aspects (health, population density) Risk combines the knowledge about hazard and vulnerability to make a quantitative prediction of the elements at risk, like numbers of lives to be possibly lost, people to be injured, cost of property being damaged and destroyed or economic activities a affected

2.1 Hazard, risk & vulnerability

In order to provide a systematic approach to study the landslide, Varnes (1984) defined various types of hazard, risk & vulnerability

Natural hazard the probability of occurrence of a potentially damaging phenomenon

within a specific period of time and within a given area

Vulnerability the degree of loss to a given element or set of elements resulting from the

occurrence of a natural phenomenon of a given magnitude

Element at risk the population, properties, economic activities etc… at risk in a given area

Risk the expected degree of loss due to a particular natural phenomenon Hence it is a

product of hazard and vulnerability

Criteria for risk assessment is represented schematically as below (Figure2-1)

Figure 2.1: Criteria for risk assessment (Disaster Preparedness and Mitigation-2002)

2.2 Landslide Hazard mapping

2.2.1 Definition

Although by the term landslide is used for mass movements occurring along a well defined slid ing surface, it has been used in literature as the most general term for all kinds of mass movements, including some with little or no true sliding, such as rock- falls, topples, and debris flows (Varnes, 1984) In this context, mass movement is used subsequently as a synonymous term for landslide, similar to slope movement

Zonation refers to the division of the land surface into areas and the ranking of these areas

according to degrees of actual or potential hazard Hence landslide hazard zonation shows potential hazard of landslides or other mass movements on a map, displaying the spatial distribution of hazard classes In general three basic principles or fundamental assumptions guide all zonation studies (Varnes 1984)

Ø The past and the present are keys to the future: Natural slop failures in the future will

Elements at risk

Persons

Structures

Landuse

Activities& Funtions

Human Vulnerability Structural Vulnerability Land Vulnerability Funtional Vulnerability

Vulnerability Assessment

Risk Assessment Hazard Zonation Hazard

Vulnerability

and present failures Thus, we have the possibility to estimate the features of potential future failure The absence of past and present failures does not mean that failures will

not occur in the future

Ø The main conditions that cause landslides can be identified: The basic cause of slope failures can be recognized, are fairly well known from several case studies and the effects of them can e rated or weighed It is possible to map correlate the contributing

factors to each other

Ø Degree of hazard can be estimated: If the condition and processes that promote instability can be identified, it is often possible to estimate their relative contribution and give them some qualitative or semi-quantitative measurement Thus, a summery of the degree of potential hazard in an area can be built up, which depends on the number

of failure including factors present, their severity, and their interaction

2.2.2 Scale of mapping for landslide hazard zonation

There are several technique for landslide hazard zonation can be applied, making use of GIS Therefore the appropriate scale on which the data is collected and the result presented varies considerably More detailed hazard maps require more detailed input data Thus the objective

of the analysis and the requires accuracy of the input data determine the scale

The following scales of analysis have been differentiated for landslide hazard zonation according to the definition by the International Association of Engineering geologists (1976):

§ National scale(<1:1,000,000)

§ Regional scale(1:100,000 – 1: 1,000,000)

§ Medium scale(1:25,000 – 1:100,000)

§ Large scale( 1:2,000 – 1:25,000)

2.3 Fundamental of Remote sensing

2.3.1 Concept of Remote Sensing

Remote sensing is defined as the science and technology by which the characteristics of the objects of interest can be identified, measured or analyzed the characteristics of the objects without direct contact

Electromagnetic radiation, which is reflected or emitted from an object, is the usual source of remote sensing data A device, to detect the electro-magnetic radiation, reflected or emitted, from an object is called a “remote sensor” or “sensor” A vehicle to carry the sensor is called a

“platform”

Remote sensing is classified into three types with respect to the wavelength regions

Ø Visible and reflective Infrared remote sensing

Ø Thermal infrared remote sensing

Ø Microwave remote sensing

2.3.2 Spectral Reflectance of Landcovers

Spectral reflectance is assumed to be different with respect to the type of landcover This is the principle that in many cases it allows the identification of landcovers with remote sensing by observing the spectral radiance from s distance far removed from the surface Fig.2.2 shows three curves of spectral reflectance for typical land covers; vegetation, soil and water As seen in the figure, vegetation has a very high reflectance in the near infrared region, though the re are three low minima due to absorption

Soil has rather higher values for almost all spectral regions Water has almost no reflectance in the infrared region

Figure 2.2: Spectral reflectance of vegetation, soil and water Figure 2.2 shows two detailed curves of leaf reflectance and water absorption Chlorophyll, contained in a leaf, has strong absorption at 0.45 m and 0.67 m, and high reflectance at near infrared (0.7-0.9 m) This results in a small peak at 0.5-0.6 (green co lor band), which makes vegetation green to the human observer

Near infrared is very useful for vegetation surveys and mapping because such a steep gradient

at 0.7-0.9 m is produced only by vegetation

Because of the water content in a leaf, there are two absorption bands at about 1.5 m and 1.9

m This is also used for surveying vegetation vigor

Figure 2.3: Spectral reflectance of a green left

2.3.3 Description of the data set-Landslide image

Database of remote sensing is used in this thesis is Landsat 7 ETM The application of satellite data has increased enormously in the past decade After the initial low-spatial resolution images of the LANDSAT MSS ( which were about 60 by 80 m), LANDSAT now has a significant improve in its characteristics with thematic mapper (TM) images It has a spatial resolution image of the 30 m and excellent spectral resolution Landsat TM provides sevens bands to cover the entire visible, near infrared and middle infrared portions of the spectrum, with one additional band providing a lower resolution of the thermal infrared (table 2-1) Landsat satellite orbits are arranged to provide good coverage of a large portion of the earth’s surface The satellite passed over each location every 18 days, offering a theoretical temporal resolution of 18 days

Table 2.1: Landsat 7 ETM image characteristic

Band Spectral range(µm) Spatial resolution(m)

2.3.4 Image interpretation

Image interpretation is defined as the extraction of qualitative and quantitative information in the form of a map, about the shape, location, structure, function, quality, condition, relationship of and between objects, etc by using human knowledge or experience

Information extraction in remote sensing can be categorized into four types which are as follows:

Classification is a type of categorization of image data using spectral, spatial and temporal information

Change detection is the extraction of change between multi-date images

Extraction of physical quantities corresponds to the measurement of temperature, atmospheric constituents, and elevation and so on from spectral

Identification of specific features is the identification, for example, of disaster, lineament and other feature etc

Figure 2.4 show a typical flow of the image interpretation process:

Figure 2.4: The image interpretation processing

2.3.5 Image Processing System

The remotely sensed data are usually digital data Together information from that we need data processing The processes are given in the chronological order:

A/D conversion using a film scanner etc

Primary Processing D/D conversion

Classified image

2.3.6 Geometric Correction

Geometric correction is undertaken to avoid geometric distortions from a distorted image, and

is achieved by establishing the relationship between the image coordinate system and the geographic coordinate system using calibration data of the sensor, measured data of position and attitude, ground control points, atmospheric condition etc

The steps to follow for geometric correction are as follows:

Figure 2.6 : The flow of geometric correction There are three methods of geometric correction as mentioned below:

-Systemmetic correction

-Non-systemmatic correction

-Combined method

2.3.7 Registration and Rectification

Refael C Gonzalez Rechard E Woods (1993) explained that the another important application

is the image registration or finding correspondence between two images The procedure for image registration is the same as the method just illustrated for geometric correc tion However, the emphasis is on transforming an image so that it will correspond with another image of the same science but viewed perhaps from other prospective

Figure 2.7: Procedures of Classification

Ø Supervised Classification

It is done in order to determine rule of classification It is necessary to know the spectral characteristics or features with respect to the population of each class The spectral features can be measured using ground based spectrometers However due to atmospheric effects, direct use of spectral features measured on the ground are not always available, for this reason, sampling of training data from clearly identified training areas, corresponding to defined classes is usually made for estimating the population statistics Statistically unbiased sampling

of training data should be made in order to represent the population correctly

Ø The minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in multi- feature space The distance is defined as an index of similarity so that the minimum distance is identical to the maximum similarity

Ø The maximum likelihood classifier is one of the most popular methods of classification in

remote sensing, in which a pixel with the maximum likelihood is classified into the

corresponding class The likelihood is defined as the posterior probability of a pixel

belonging to class k

2.3.9 Spatial Filtering

Spatial filtering is used to obtain enhanced images or improved images by apply ing, filter

function or filter operators in the domain of the image space (x,y) or spatial frequency (x,h) Spatial filtering in the domain of image space aims at image enhancement with so-called enhancement filters, while in the domain of spatial frequency it aims at reconstruction with so-called reconstruction filters, which is in the domain of spatial frequency it aims at reconstruction with so called reconstruction filters An output image from filtering of spatial pass filters, band pass filter etc are typical filters with frequency control Low pass filters which out puts only lower frequency, noise, while high pass are used for example stripe noise

of low frequency

2.4 GIS overview

2.4.1 Geographic Information System

According to Burrough (1986), Geographic Information system (GIS) is a powerful set

of tools for collecting, retrieving, transforming, and displaying spatial data from the real world for a particular set of perposes

Aronoff (1993) states that GIS is designed for the collection, storage and analysis of objects and phenomena where geographic location is critical to analysis For example, the location of a fire station or the locations where soil erosion is most severe are key considerations in using information In each case, what it is and where it is must be taken into account

2.4.2 Terrain Modeling for Mountain

Mountain may be defined as dynamic system in which both the extent and variability

in relief are key controlling elements Altitude, aspect and slope strongly both the human and the physical characteristics of mountain ecosystems, such as the distribution of agriculture, the type of forestry, micro and local climates and the extent of the mass movement A model of relief is therefore an essential component of a mountain GIS At present the most powerful method of representing relief is to construct a mathematical model of the earth’s surface: a digital terrain model( DTM) or digital elevation model(DEM) This mathematical model can

be used to drive information on height, aspect, slope, angle, watersheds, hill shadows and cut and fill estimates which may be essential components of management plan or inputs to a process model

Any digital representation of continuous variation of relief over space is known as a DEM DEMs were originally developed for modeling relief; they can of course be used to model the continuous variation of any other attribute Z over two dimensional surfaces (Burrough, 1986)

2.5 Global Positioning System (GPS)

The U.S Department of defense developed a navigatio n system called Global Positioning System “GPS” It is based on the 24 satellites which orbit around the earth at an altitude of 20,200 km the satellites are high enough to avoid land based system problems With this technology one can find the location of an object any where in the world 24hrs day The accuracy for measurement with GPS is from 5 to 10 meters range With differential post processing the accuracy can be few millimeter

GPS is digital electronic equipment based on satellite ranging; it means we can figure out our position on earth by measuring our distance from a group of satellites in space The satellites act as a reference point

There are atom clocks on the satellites which are show accurate time, due to which we can eliminate any error caused by the watch of the GPS receiver

2.6 Web Map Server

2.6.1 Introduction

Open Source software and its specifications are the foundation of this study PostgreSQL, the

extension which makes the PostgreSQL connected to the Minnesota MapServer in order to display the database in various formats Minnesota MapServer is used to explore GIS data over World Wide Web (WWW)

Open Source technology is base on the premise tha t the programming source code is freely available to anyone who wishes to read, add to, or even modify and redistribute the computer software code Open source technology offers a level of stability and flexibility that is not typically available with “out-of-the-box” software

Benefit of Open Source Technologies

Ø Free to use- there are no licensing fees

Ø The software can be duplicated and installed on as many machines in as many environments, with no restrictions

Ø Full access to source code

Ø Highly responsive to end user requirements

Open source does not just mean access to the source code The distribution terms of open- source software must comply with the following criteria:

a Free redistribution

b Source Code

c Derived Works

d Integrity of the Author’s Source Code

e No Discrimination against Persons or Groups

f License Must Not Be Specific to a Product.(Open GIS)

Minnesota Map Server provides Open GIS Consortium’s (OGC) Web map Service(WMS) and Web Feature Service These two specifications will use operation on the client’s request to produce maps of georeferenced data in various formats such as JPEG, PNG, GIF and GML

2.6.2 Open GIS Standard

The OpenGIS Standard specifies the behavior of a service that produces georeferenced maps This standard specifies operations to retrieve a description of the map offered by a service instance, to retrieve a map, and to query a server about features displayed on a map OpenGIS Standard is applicable to pictorial renderings of maps in a graphical format This standard is not applicable to retrieval of actual feature data or coverage data values

1 Web Map Service (WM S) Implementation Specification

A Web Map Service produces maps of georeferenced data We defined a ‘Map” as a visua l representation of geodata; a map is not the data itself These maps are generally rendered in a pictorial format such as PNG, GIF or JPEG, or occasionally as vector-based graphical elements in Scalable Vector Graphics (SVG) or Web Computer Graphics Metafile (WebCGM) formats This specification standardizes the way in which maps are requested by client and the way that servers describe their data holdings The three operations are as follows:

Ø GetCapabilities (Required): Obtain service- level metadata, which is a machine readable (and human- readable) description of the WMS’s information content and acceptable request parameters

Ø GetMap (required): Obtain a map image whose geospatial and dimensional parameter are well-defined

Ø GetFeatureInfo( optional): Ask for information about par ticular features shown on a map

A standard web browser can ask a Web Map Service to perform these operations simply by submitting requests in the form of Uniform resource Location (URLs) The content of such URLs depends on which of the task is requested All URLs include a specification version number and a request type parameter In addition, when invoking GetMap a WMS Client can specify the information to be shown on the map (one or more” Layers”), possibly the “style”

of those Layers, what portion of the Earth is to be mapped (a “ Bounding Box”), the projected

or geographic coordinate reference system to be used (the “ Spatial reference System, “or SRS), the desired output format, the output size (Width and Height), and background transparency and color When invoking GetfeatureInfo the Client indicates what map is being queried and which location on the map is of interest

2 Web Feature Service (WFS) Implementation Specification

The OGC Web Map Service allows a client to overlay map images display ser ved from multiple Web Map Services on the Internet In a similar fashion, the OGC Web Feature Service allows a client to retrieve geospatial data encoded in Geography Markup Language (GML) from multiple Web Feature Services

The WFS operations support INSERT, UPDATE, DELETE, QUERY and DISCOVERY operations on geographic features using HTTP as the distributed computing platform

Figure 2.8: WFS Processing Request

Client

Web Feature Service (WFS)

2.7 Landslide Studies

-Kwang -Hoon Chi*, Kiwon Lee**, and No-Wook Park*(2001), studied Landslide

Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability ana lysis with actual landslide occurrences at Boeun area, Korea, and then landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scarped scale of landslide occurrences These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results

in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences Therefore, it is expected that the results on the space robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences

-D.Z.Seker, M.O.Altan(2001) used various types of data to extract relevant information

This study is to determine a suitable methodology for predicting possible landslide areas and producing landslide risk map in the study area of Sebinkkarahisar Township, which is located

at the northeastern part of Turkey These include the satellite sensor data taken in the year of

1987 and 2000, which are use for the extraction of land surface temperature and landuse information 1:25000 scale standard topographic map has been digitized and the obtained contours were used for the derivation of DEM and slope map of the study site Satellite images, DEM and slope map of the study area were used to investigate the possible landslide risk areas and reason of this natural hazard which threat the study area frequently

-Nguyen Quoc Phi, Bui Hoang Bac (2000) gives a view of landslide characteristics on

natural terrain of YangSan area, Korea and developing a GIS approach to modeling slope instability The relations between landslide distributions with the physical parameters such as lithology, elevation, slope gradient, slope aspect, lineament, drainage, vegetation, and land sue were analy zed by Bayesian statistical model A susceptibility map is modeled by incorporating these in weight of evidence model using Bayesian approach

-Atsushi Kajiyama, Takamoto, Truong Xuan Luan and Nhu Viet Ha (2002) used

stereo-photogrammetry technique (using Photomodeler software) application for monitering a landslide twice in Monset area, northwestern part of Viet Nam in 2002 to 2003 this technique has allowed us to deriver surface deformation maps of landslide with a high spatial resolution and accuracy Photomodeler software can treat it easily using reference point and the photograph, which they have been taken photograph with common digital camera Using this software, we estimated the amount of movements of whole landslide for one year The result has been validated by comparing independent measurements carried out by laser telemeter

-Mike Doratti, Chris McColl and Claire Tweeddale (2002) applied GIS to predict

landslide hazard areas following clearcut logging events The landslide prediction study project sponsor is Tom Millard of BC Ministry of forest The objective of this project is to produce a report and maps identifying potential landslide hazard areas within the Cascade

Mountain region, Britsh Columbia Historically, use of GIS technologies in this area of Forest Resource Management have been limited, therefore application of this software could potentially improve current practices Slope stability factors were derived from TRIM digital elevation models Algorithms were developed to create a landslide hazard model This model was compared to existing landslide data, and to the current terrain stability mapping standard

to assess model accuracy

-Amod Sagar, Takaaki Amoda and Masamu Aniya (1997) This paper presents the

development of landslide susceptibility mapping using GIS The study area is Kulekhan watershed lying in the lesser Himalayan region of Nepal A landslide distribution map produced from interpretation and field work was used to analyzed the important factors to landslides, employing Quantification scaling type 2(Q-S2) method The six factor used were slope gradient, slope aspect, elevation geology, landuse/cover, and drainage basin orde Overlaying the factors with scores for their classes computed by the analysis, landslide susceptibility maps were produced with classes of high, moderate, less and least susceptible

-Richard Kho Shu Yuan and Mohd Ibrahim (1997) In this study, land surface

temperate and landuse information have been derived from Landsat thematic Mapper data The elevation and slope inclination have been determined from DEMs generated from aerial photographs Underground water level information has been obtain from the combination of above data From these data, simple algorithms were used to classify the area into different risk zones By combining all the risk maps using GIS techniques, final risk maps were produced which take into account all above factors

-Purna, Dr kaew, Dr Jean Delsol, P Gupta, Prinya (1995).The methodology is used

for landslide hazard zonation Landslide distribution is overlaid with other landslide influencing spatial parameters slope, aspect, and land system, landuse, bedrock, isohyetal and seismic zonation, amd first order buffered stream map The slope and aspect maps were generated from the DTM The land use map and landslide map were produced from aerial photo interpretation

Figure 2.9: Map of large Landslide areas of Vietnam

STUDY AREA

CHAPTER 3 DESCRIPTION OF THE STUDY AREA 3.1 Area and situation

Xin Man district is situated in the South West of Ha Giang province The study area lies geographically between 22010’ N to 23030’N and 104020’E to 105034’E

Xin Man and Hoang Su Phi districts lie in the high land and accounting for 18.3% of the

total province’s area and 17.2% of the population The potential for the area is to develop plants for derived resin

3.2 Climate

The climate is divided in two distinct seasons (rainy and dry), although it tends to vary depending on altitude The annual average temperature varies between 24 and 28°C In winter, the temperature is sometime -5oC

3.3 Rainfall

The mean annual rainfall, according to the records of Metrological station, for the 20 years (1985-2005) is about 1,695 mm Most of the rain comes in the months of August and September In that time, the intensity rain fall is 2000-2500mm in some high mountains (>1500m) and causing flash flood and landslide

0 500 1000 1500 2000 2500

The population census was carried out in 2000 Total area is 665.25sq.km and its population is

43926 habitants There are 22 communes in this district They are Ban Diu, Ban Ngo, Chien Pho, Che la, Chi Ca, Coc Pai, Coc Re, Khuon Lung, Ngan Chien, Na Chi, Nan Ma, Nan Xin, Nam Dan, Pa Vay Su, Quang Nguyen, Thu Ta, Then Phang, Trung Thinh, Tan Nam, Ta Cu

Ty, Ta Nhiu, Xin Man

Figure 3.2: Location of Study area Xin Man district, Viet Nam

Map 3.1: Tin in Xin Man district

Elevation and slope maps are extracted from TIN which has been obtained from the Contour map in Xin Man district The TIN is show in the map 3.1

- The gravelly soil is not combining together and having different component, size live

in value of river and stream, and the slope surface This gravelly soil group is very development in the watershed, including is sediment slope, sediment flood and smaller than rising than level of sediment

- Rock weathering very strong including metamorphism two mica schist, granite is compressed

- All granite is not weathering

All of group gravelly are also moved by the earth’s crust making many local are compressing, catalectic, breaking, and create advantage condition for landslide and debris flow

In the study area, the vario us litho-stratigraphic units were prouped into four categories It is shown in table 3.12 & figure 3.12

Table 3.1: Geology, major litho-stratigraphic units with their corresponding classes Geology Major litho-stratigraphic units

Marble, motley limestone, clayish limestone, clay-sericite schist

Fine-to medium-grained, porphyritic biotite granite

Coarse grained gneissoid biotite granite

Graphite-bearing marble, two- mica schist, quartz- mica schist, quartzite, epidote-biotite schist, epidote-biotite-hornblende schist, thin beds of marble

Table 3.2: Area under Geology

Classes Total are(km2) Area in %

Map 3.2: Geological map in Xin Man district

3.6 Elevation

The area comprises of high mountain ranging from elevation 200m to 2400m About 67% of study area lies in the range of elevation from 500m to 1500m Remaining 10% of area has a very high range of elevation from 1500m to 2400m The elevation range extracted from the digitized contour map of scale 1:50 000 is given in the following table 3.1 and illustrated in the figure 3.4 Elevation range for the study area is obtained from the Digital Elevation Model (DEM) And it is shown in the Map 3.1

Table 3.3 : Area under Elevation Elevation classes(m) Total area (km2) Area in %

Total elevation area in %

1500-2000 0-300 2000-2400 300-500 1000-1500 500-1000

Elevation class

Figure 3.4: Elevation chart

Map 3.3: Elevation map in Xin Man district

3.7 Slope

Due to presence of rugged terrain, occurrence of landslide and field survey in the Xin Man the slope is divided into following six classes

Table 3.4: Area under slope

Slope classes(degree) Total area(km2) Area in %

Source: Contour Map of scale 1:50 000 and Digital terrain model

From the digital information of slope it has been obverted that 76.3% of area has slope range

of 15-45 degree and only 5.17% has slope range more than 45 degree The percent of area under different slope range are given in table 3.6 and slope variations are shown graphically in figure 3.5 The slope map has been extracted from the TIN and slope classes are shown in the map 3.3

Map 3.4: Slope map in Xin Man district

3.8 Lineament

The lineaments are the linear morpho-techtonic feature of the terrain which include faults, fractures, ridges, major discontinuities etc (S.Sarkar, 2003) In this work, lineament were found depending on geology map This area are developed fault system of NW-SE, ME-SW and sublatitudinal faults

-NW-SE trending faults system is developed contrentratesly in the northeast Together with NW-SE trending fault system they form faults of feather form in the southeast of the Chay river Granite Massif

- Sublatitudinal faults from the boundary between the Chay river Granite massif and Cambrian-Devonian sediments

The lineament are buffered in different distance The distances to lineament factor was computed using analyst extension of arcview and were classified as table 3.5 below

Table 3.5: Area under distance to lineament

Distance classes(m) Total area Area in %

Map 3.5 shows the distance to lineament classes in different color

10.46 11.93 13.65

16.11 17.13

30.72

05101520253035

Map 3.5: Distance to lineament map in Xin Man

3.9 Road system

The study area has one major road having width of 8 m A large number of houses are connected to the road So we must to create buffer to the road with different distance The results are given in the table 3.6

Table 3.6: Area under distance to road

Distance classes(m) Total area(km2) %Area

It has been observed that the 73.23% of road lies under distance range more than 150 m, only 2.51% of area belong to range 0-10m Total range from 10-150 m occupied nearly 25% of the area Road network in Xin Man district is shown in the Map 3.6 The distance range variation for the buffer road is shown in the figure 3.7

2.51 3.48 4.38 4.69

11.71

73.23

0 10 20 30 40 50 60 70 80

0-10 10-25 50-75 25-50 75-150 >150

Distance classes

Figure 3.7: Road area under the buffer

Map 3.6: Buffer road system map in Xin Man

3.10 Drainage density

Drainage density is defined as the ratio of sum the drainage length in the cell and the area of the corresponding cell (S.Sarkar, 2003) The under cutting action of the river may include the effect of this causative factor and converted into raster format The drainage density (figure 3.10) was computed considering a 20 by 20 m cell and classified with intervals as show in table 3.6

Table 3.7: Area under drainage density

Drainage density(m) Total area(km2) % of Area

Drainage density classes

Figure 3.8: Drainage density chart

Map 3.7: Drainage density map in Xin Man

3.11 Landcover

Landcover in Xin Man is classified into five categories, out of which dense forest bears the highest percentage of the area (36.57%) Therefore, bareland occupied one of third of the area 30.7% Landcover classes are given in the table 3.10 & figure 3.10 The landcover information, extracted from the satellite data, is shown in the Map 3.9

Table 3.8: Area under Landcover

Landcover classes Total area(km2) %of Area